In the United States (US), stroke affects more than 700,000 people annually in the U.S. and is the third leading cause of death. Stroke patients may present to the emergency room with a wide variety of symptoms that can be caused by other conditions not related to stroke, collectively called stroke mimic. Even at the most advanced stroke centers, the diagnosis of ischemic stroke is made by clinical examination after ruling out hemorrhagic lesions by imaging. These imaging tests can be used to diagnose hemorrhagic stroke but is relatively ineffective in detecting most ischemic stroke. A reliable and rapid method to distinguish stroke and stroke mimic is a necessary aid to current examination and imaging-based stroke diagnosis and will become more crucial with the implementation of new therapies for the time-critical treatment of ischemic stroke. In contrast to cardiovascular disease and oncology, no approved immunoassay or molecular test is available to aid diagnosis. Analysis of a previous dataset demonstrated that our advanced GeneRx algorithm technologies were able to develop an accurate diagnostic model where other multi-marker panels failed. In this phase I proposal we will perform a validation study, collecting blood samples from 200 admitted with stroke-like symptoms and diagnosed as stroke or stroke mimic. We will measure stroke biomarker protein levels markers present in the top combinations and algorithms from our previous study, and utilize our proprietary GeneRx technology to develop a multiple marker predictive model for accurate differentiation of stroke from stroke mimic. Project Narrative: Currently no blood test is available to help accurately diagnose stroke patients. This study develops a diagnostic to help differentiate stroke patients from the many non-stroke patients that arrive at the ER with stroke-like symptoms. Successful development of this diagnostic will increase the effectiveness and speed of stroke treatment to increase a patient's chances of improvement and safety of treatment. [unreadable] [unreadable] [unreadable]